A novel approach for protein structure prediction based on an estimation of distribution algorithm

  • Amir Morshedian
  • Jafar Razmara
  • Shahriar Lotfi
Methodologies and Application


Protein structure prediction is one of the major challenges in structural biology and has wide potential applications in biotechnology. However, the problem is faced with a difficult optimization requirement with particularly complex energy landscapes. The current article aims to present a novel approach namely AHEDA as an evolutionary-based solution to overcome the problem. AHEDA uses the hydrophobic-polar model to develop a robust and efficient evolutionary-based algorithm for protein structure prediction. The method utilizes an integrated estimation of distribution algorithm that attempts to optimize the search process and prevent the destruction of structural blocks. It also uses a stochastic local search to improve its accuracy. Based on a comprehensive comparison with other existing methods on 24 widely used benchmarks, AHEDA was shown to generate highly accurate predictions compared to the other similar methods.


Estimation of distribution algorithm (EDA) Protein structure prediction (PSP) HP model Protein folding Stochastic local search (SLS) 


Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Amir Morshedian
    • 1
  • Jafar Razmara
    • 1
  • Shahriar Lotfi
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematical SciencesUniversity of TabrizTabrizIran

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